东北电力技术期刊论文格式

1. 发电企业减排CO_2的技术经济对策 张近朱 文献来自: 东北电力技术 2002年 第06期 CAJ下载 PDF下载 通过费用和效果(O飞减排量)的比较,分析判断减排措施的优劣。减排技术一般分为专门减排技术。辅助减排技术和替代技术3大类。技术类别不同,COZ减排的数量亦不同。减排技术的费用指标为:以现行COZ排放量为基准,凡形成CQZ排放量下降的 ... 被引用次数: 1 文献引用-相似文献-同类文献 2. 蒸气压缩式集中空调机组能源效率标准的研究 卢苇 文献来自: 天津大学 2004年 博士论文 CAJ下载 在线阅读 分章下载 分页下载 将来必然要履行温室气体减排的义务。“十五”期间我国的能源发展战略就指出要提高能源效率,在坚持合理利用资源的同时,努力提高能源生产、消费效率,以促进经济增长,提高人民生活质量。同时在能源的生产、消费过程中都要注意环境质量的要求,实现能 ... 被引用次数: 4 文献引用-相似文献-同类文献 3. 农村可再生能源建设对减排CO_2的贡献及行动 王革华 文献来自: 江西能源 2002年 第01期 CAJ下载 PDF下载 被引用次数: 5 文献引用-相似文献-同类文献 4. 锅炉与CO_2减排 俞建洪 文献来自: 工业锅炉 2004年 第01期 CAJ下载 PDF下载 不规定具体的减排指标,但要努力减排;对38个发达国家CO2减排量作了明确规定:其中欧共体国家到2012年CO2排放量要比1990年降低8%,美国降低7%,日本降低6%,其他发达国家和东欧降低5 ... 被引用次数: 1 文献引用-相似文献-同类文献 5. 温室气体减排与21世纪我国的能源发展战略 魏东,马一太,吕灿仁 文献来自: 能源技术 2001年 第02期 CAJ下载 PDF下载 通过合理选择低减排成本的项目 ,可以使全球在实现相同减排目标的前提下 ,大大减少总的减排成本。图 3是几组国家工业CO2 减排的供给曲线示意图[5] ,从图中可看出 ,中国和?... 被引用次数: 2 文献引用-相似文献-同类文献 6. 火力发电企业的成本管理 李建飞 文献来自: 华北电力大学(北京) 2006年 硕士论文 CAJ下载 在线阅读 分章下载 分页下载 火 力发电企业的燃料主要是燃煤和燃油,燃料成本在火力发电企业的总成本中占 50%一80%左右。如图2一1所示:2001一2005年国华盘山电厂的燃料成本在总成本中 比率燃料费44%一59%、折旧费占19%一28% ... 被引用次数: 0 文献引用-相似文献-同类文献 7. 试论建筑节能的新观念 龙惟定 文献来自: 暖通空调 1999年 第01期 CAJ下载 PDF下载 议定书确定了各缔约方到2010年所承担的包括CO2在内的6种温室气体的减排量(见表2)。表2京都议定书规定的各国温室气体减排量国家与1990年相比的减排量/%澳大利亚增加8奥地利,比利时,保加利亚,捷克,丹麦,爱沙尼亚,欧盟,芬兰,法国,德国,希腊,爱尔兰,意大利,拉脱维亚,列支 ... 被引用次数: 30 文献引用-相似文献-同类文献 8. 新建火力发电企业市场化运作管理研究 刘胜金 文献来自: 西南交通大学 2004年 硕士论文 CAJ下载 在线阅读 分章下载 分页下载 电力工业是国民经济的基础产业,高效、可靠的电力工业对其它产业的快速发展起着重要的作用。随着中国电力市场化发展和电力体制改革的不断深入,电力企业面临着严峻的挑战。特别是新建火力发电企业,要想在激烈的市场竞争中立于不败之地,就必须适应电力市场化发展要求,实行市... 被引用次数: 0 文献引用-相似文献-同类文献 9. 全球变暖与二氧化碳减排 裴克毅,孙绍增,黄丽坤 文献来自: 节能技术 2005年 第03期 CAJ下载 PDF下载 行动的减排成本普遍低于其他减排方案。3碳税与二氧化碳减排利用税收杠杆保护环境,对破坏、有损环境的生产经营活动课税。是欧洲?... 被引用次数: 0 文献引用-相似文献-同类文献 10. 供电企业电能质量管理与节能降耗问题研究 唐占荣 文献来自: 华北电力大学(北京) 2005年 硕士论文 CAJ下载 在线阅读 分章下载 分页下载 节能降耗 Firstly, this paper commences from the related problems in power quality, which includethe ... 被引用次数: 0 文献引用-相似文献-同类文献 搜火力发电 的学术趋势 搜企业 的学术趋势 搜索相关数字 山东省火力发电 江苏省火力发电量 全国火力发电量 参考资料：中国知网

Sensorless torque control scheme ofinduction motor for hybrid electric vehicleYan LIU 1,2, Cheng SHAO1(1.Research Institute of Advanced Control Technology, Dalian University of Technology, Dalian Liaoning 116024, China;2.School of Information Engineering of Dalian University, Dalian Liaoning 116622, China)Abstract: In this paper, the sensorless torque robust tracking problem of the induction motor for hybrid electric vehicle(HEV) applications is addressed. Because motor parameter variations in HEV applications are larger than in industrialdrive system, the conventional field-oriented control (FOC) provides poor performance. Therefore, a new robust PI-basedextension of the FOC controller and a speed-flux observer based on sliding mode and Lyapunov theory are developed inorder to improve the overall performance. Simulation results show that the proposed sensorless torque control scheme isrobust with respect to motor parameter variations and loading disturbances. In addition, the operating flux of the motor ischosen optimally to minimize the consumption of electric energy, which results in a significant reduction in energy lossesshown by simulations.Keywords: Hybrid electric vehicle; Induction motor; Torque tracking; Sliding mode1 IntroductionBeing confronted by the lack of energy and the increasinglyserious pollution, the automobile industry is seekingcleaner and more energy-efficient vehicles.A Hybrid ElectricVehicle (HEV) is one of the solutions. A HEV comprisesboth a Combustion Engine (CE) and an Electric Motor(EM). The coupling of these two components can be inparallel or in series. The most common type of HEV is theparallel type, in which both CE and EM contribute to thetraction force that moves the vehicle. Fig1 presents a diagramof the propulsion system of a parallel HEV [1].Fig. 1 Parallel HEV automobile propulsion system.In order to have lower energy consumption and lower pollutantemissions, in a parallel HEV the CE is commonlyemployed at the state (n > 40 km/h or an emergency speedup), while the electric motor is operated at various operatingconditions and transient to supply the difference in torquebetween the torque command and the torque supplied bythe CE. Therefore fast and precise torque tracking of an EMover a wide range of speed is crucial for the overall performanceof a HEV.The induction motor is well suited for the HEV applicationbecause of its robustness, low maintenance and lowprice. However, the development of a drive system basedon the induction motor is not straightforward because of thecomplexity of the control problem involved in the IM. Furthermore,motor parameter variations in HEV applicationsare larger than in industrial drive system during operation[2]. The conventional control technique ranging from theinexpensive constant voltage/frequency ratio strategy to thesophisticated sensorless control schemes are mostly ineffectivewhere accurate torque tracking is required due to theirdrawbacks, which are sensitive to change of the parametersof the motors.In general, a HEV operation can be continuing smoothlyfor the case of sensor failure, it is of significant to developsensorless control algorithms. In this paper, the developmentof a sensorless robust torque control system for HEVapplications is proposed. The field oriented control of the inductionmotor is commonly employed in HEV applicationsdue to its relative good dynamic response. However the classical(PI-based) field oriented control (CFOC) is sensitive toparameter variations and needs tuning of at least six controlparameters (a minimum of 3 PI controller gains). An improvedrobust PI-based controller is designed in this paper,Received 5 January 2005; revised 20 September 2006.This work was supported in part by State Science and Technology Pursuing Project of China (No. 2001BA204B01).Y. LIU et al. / Journal of Control Theory and Applications 2007 5 (1) 42–46 43which has less controller parameters to be tuned, and is robustto parameter variation.The variable parameters modelof the motor is considered and its parameters are continuouslyupdated while the motor is operating. Speed andflux observers are needed for the schemes. In this paper,the speed-flux observer is based on the sliding mode techniquedue to its superior robustness properties. The slidingmode observer structure allows for the simultaneous observationof rotor fluxes and rotor speed. Minimization of theconsumed energy is also considered by optimizing operatingflux of the IM.2 The control problem in a HEV caseThe performance of electric drive system is one of thekey problems in a HEV application. Although the requirementsof various HEV drive system are different, all thesedrive systems are kinds of torque control systems. For anideal HEV, the torque requested by the supervisor controllermust be accurate and efficient. Another requirement is tomake the rotor flux track a certain reference λref . The referenceis commonly set to a value that generates maximumtorque and avoids magnetic saturation, and is weakened tolimit stator currents and voltages as rotor speed increases.In HEV applications, however, the flux reference is selectedto minimize the consumption of electrical energy as it is oneof the primary objectives in HEV applications. The controlproblem can therefore be stated as the following torque andflux tracking problems:minids,iqs,we Te(t) − Teref (t), (1)minids,iqs,we λdr(t) − λref (t), (2)minids,iqs,we λqr(t), (3)where λref is selected to minimize the consumption of electricalenergy. Teref is the torque command issued by thesupervisory controller while Te is the actual motor torque.Equation (3) reflects the constraint of field orientation commonlyencountered in the literature. In addition, for a HEVapplication the operating conditions will vary continuously.The changes of parameters of the IM model need to be accountedfor in control due to they will considerably changeas the motor changes operating conditions.3 A variable parameters model of inductionmotor for HEV applicationsTo reduce the elements of storage (inductances), the inductionmotor model used in this research in stationary referenceframe is the Γ-model. Fig. 2 shows its q-axis (d-axisare similar). As noted in [3], the model is identical (withoutany loss of information) to the more common T-model inwhich the leakage inductance is separated in stator and rotorleakage [3]. With respect to the classical model, the newparameters are:Lm = L2mLr= γLm, Ll = Lls + γLlr,Rr = γ2Rr.Fig. 2 Induction motor model in stationary reference frame (q-axis).The following basic w−λr−is equations in synchronouslyrotating reference frame (d - q) can be derived from theabove model.⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩dλdrdt= −ηλdr + (we − wr)λqr + ηLmids,dλqrdt= −(we − wr)λdr − ηλqr + ηLmiqs,didsdt= ηβλdr+βwrλqr−γids+weiqs+1σLsVds,diqsdt=−βwrλdr+ηβλqr−weids−γiqs+1σLsVqs,dwrdt= μ(λdriqs − λqrids) −TLJ,dθdt= wr + ηLmiqsλdr= we,Te = μ(λdriqs − λqrids)(4)with constants defined as follows:μ = npJ, η = RrLm, σ = 1−LmLs, β =1Ll,γ = Rs + RrLl, Ls = Ll + Lm,where np is the number of poles pairs, J is the inertia of therotor. The motor parameters Lm, Ll, Rs, Rr were estimatedoffline [4]. Equation (5) shows the mappings between theparameters of the motor and the operating conditions (ids,iqs).Lm = a1i2ds + a2ids + a3, Ll = b1Is + b2,Rr = c1iqs + c2.(5)4 Sensorless torque control system designA simplified block diagram of the control diagram isshown in Fig. 3.44 Y. LIU et al. / Journal of Control Theory and Applications 2007 5 (1) 42–46Fig. 3 Control structure.4.1 PI controller based FOC designThe PI controller is based on the Field Oriented Controller(FOC) scheme. When Te = Teref, λdr = λref , andλqr = 0 in synchronously rotating reference frame (d − q),the following FOC equations can be derived from the equations(4).⎧⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎩ids = λrefLm+ λrefRr,iqs = Terefnpλref,we = wr + ηLmiqsλref.(6)From the Equation (6), the FOC controller has lower performancein the presence of parameter uncertainties, especiallyin a HEV application due to its inherent open loopdesign. Since the rotor flux dynamics in synchronous referenceframe (λq = 0) are linear and only dependent on thed-current input, the controller can be improved by addingtwo PI regulators on error signals λref − λdr and λqr − 0 asfollowids = λrefLm+ λrefRr+ KPd(λref − λdr)+KId (λref − λdr)dt, (7)iqs = Terefnpλref, (8)we = wr + ηLmiqsλref+ KPqλqr + KIq λqrdt. (9)The Equation (7) and (9) show that current (ids) can controlthe rotor flux magnitude and the speed of the d − q rotatingreference frame (we) can control its orientation correctlywith less sensitivity to motor parameter variations becauseof the two PI regulators.4.2 Stator voltage decoupling designBased on scalar decoupling theory [5], the stator voltagescommands are given in the form:⎧⎪⎪⎪⎨⎪⎪⎪⎩Uds = Rsids − weσLsiqs = Rsids − weLliqs,Uqs = Rsiqs + weσLsids + LmLrweλref= Rsiqs + weσLsids + weλref .(10)Because of fast and good flux tracking, poor dynamics decouplingperformance exerts less effect on the control system.4.3 Speed-flux observer designBased on the theory of negative feedback, the design ofspeed-flux observer must be robust to motor parameter variations.The speed-flux observer here is based on the slidingmode technique described in [6∼8]. The observer equationsare based on the induction motor current and flux equationsin stationary reference frame.⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩d˜idsdt= ηβ˜λdr + β ˜ wr˜λqr − γ˜ids +1LlVds,d˜iqsdt= −β ˜ wr˜λdr + ηβ˜λqr − γ˜iqs +1LlVqs,d˜λdrdt= −η˜λdr − ˜ wr˜λqr + ηLm˜ids,d˜λqrdt= ˜wr˜λ dr − η˜λqr + ηLm˜iqs.(11)Define a sliding surface as:s = (˜iqs − iqs)˜λdr − (˜ids − ids)˜λqr. (12)Let a Lyapunov function beV = 0.5s2. (13)After some algebraic derivation, it can be found that when˜ wr = w0sgn(s) with w0 chosen large enough at all time,then ˙V = ˙s · s 0. This shows that s will converge tozero in a finite time, implying the stator current estimatesand rotor flux estimates will converge to their real valuesin a finite time [8]. To find the equivalent value of estimatewr (the smoothed estimate of speed, since estimate wr is aswitching function), the equation must be solved [8]. Thisyields:˜ weq = wr˜λqrλqr + λdr˜λdr˜λ2qr +˜λ2dr −ηnp˜λqrλdr − λqr˜λdr˜λ2qr +˜λ2dr. (14)The equation implies that if the flux estimates converge totheir real values, the equivalent speed will be equal to thereal speed. But the Equation (14) for equivalent speed cannotbe used as given in the observer since it contains unknownterms. A low pass filter is used instead,˜ weq =11 + s · τ˜ wr. (15)Y. LIU et al. / Journal of Control Theory and Applications 2007 5 (1) 42–46 45The same low pass filter is also introduced to the systeminput,which guarantees that the input matches the feedbackin time.The selection of the speed gain w0 has two major constraints:1) The gain has to be large enough to insure that slidingmode can be enforced.2) A very large gain can yield to instability of the observer.Through simulations, an adaptive gain of the slidingmode observer to the equivalent speed is proposed.w0 = k1 ˜ weq + k2. (16)From Equation (11), the sliding mode observer structureallows for the simultaneous observation of rotor fluxes.4.4 Flux reference optimal designThe flux reference can either be left constant or modifiedto accomplish certain requirements (minimum current,maximum efficiency, field weakening) [9,10]. In this paper,the flux reference is chosen to maximum efficiency at steadystate and is weaken for speeds above rated. The optimal efficiencyflux can be calculated as a function of the torquereference [9].λdr−opt = |Teref| · 4Rs · L2r/L2m + Rr. (17)Equation (17) states that if the torque request Teref iszero, Equation (8) presents a singularity. Moreover, theanalysis of Equation (17) does not consider the flux saturation.In fact, for speeds above rated, it is necessary toweaken the flux so that the supply voltage limits are not exceeded.The improved optimum flux reference is then calculatedas:⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩λref = λdr-opt,if λmin λdr-opt λdr-rated ·wratedwr-actual,λref = λmin, if λdr-opt λmin,λref = λdr-rated ·wratedwr-actual,if λdr-opt λdr-rated ·wratedwr-actual.(18)where λmin is a minimum value to avoid the division byzero.4.5 SimulationsThe rated parameters of the motor used in the simulationsare given byRs = 0.014 Ω, Rr = 0.009 Ω, Lls = 75 H,Llr = 105 H, Lm = 2.2 mH, Ls = Lls + Lm,Lr = Llr + Lm, P = 4, Jmot = 0.045 kgm2,J = Jmot +MR2tire/Rf, ρair = 1.29, Cd = 0.446,Af = 3.169 m2, Rf = 8.32, Cr = 0.015,Rtire = 0.3683 m, M = 3000 kg, wbase = 5400 rpm,λdr−rated = 0.47 Wb.Fig.4 shows the torque reference curve that representstypical operating behaviors in a hybrid electric vehicle.Fig. 4 The torque reference curve.Load torque is modeled by considering the aerodynamic,rolling resistance and road grade forces. Its expression isgiven byTL = RtireRf(12ρairCdAfv2 +MCr cos αg +M sin αg).Figures in [5∼8] show the simulation results of thesystem of Fig.3 (considering variable motor parameters).Though a small estimation error can be noticed on the observedfluxes and speed, the torque tracking is still achievedat an acceptable level as shown in Figs. [5, 6, 8]. The torquecontrol over a wide range of speed presents less sensitivityto motor parameters uncertainty.Fig.5 presents the d and q components of the rotor flux.Rotor flux λr is precisely orientated to d-axis because of theimproved PI controllers.Fig.8 shows clearly the real and observed speed in thedifferent phases of acceleration, constant and decelerationspeed with the motor control torque of Fig.4. The variablemodel parameters exert less influence on speed estimation.Fig.7 shows the power loss when the rotor flux keeps constantor optimal state. A significant improvement in powerlosses is noticed due to reducing the flux reference duringthe periods of low torque requests.Fig. 5 Motor rotor flux λr.46 Y. LIU et al. / Journal of Control Theory and Applications 2007 5 (1) 42–46Fig. 6 Motor torque.Fig. 7 Power Losses.Fig. 8 Motor speed.5 ConclusionsThis paper has described a sensorless torque control systemfor a high-performance induction motor drive for aHEV case. The system allows for fast and good torquetracking over a wide range of speed even in the presence ofmotor parameters uncertainty. In this paper, the improvedPI-based FOC controllers show a good performance in therotor flux λdr magnitude and its orientation tracking. Thespeed-flux observer described here is based on the slidingmode technique, making it independent of the motor parameters.Gain adaptation of the speed -flux observer is used tostabilize the observer when integration errors are present.

呵呵，从具体的情况来看，好象这类的问题应该有专业方面的人才来帮助你回答啊！很可惜我不会啊！

Robotics education in the university* Rafael M. Inigo and Jose M. Angulo School of Engineering and Applied Science, University of Virginia, Charlottesville, Virginia 22901, USADept. de Informatica, Universidad de Deusto, Bilbao, Spain Available online 28 October 2004. The importance of automation and robotics in modern factories has required the introduction of courses on these subjects at the graduate and undergraduate levels in engineering schools. A comprehensive course on robotics must include the following subjects of fundamental importance: kinematics, dynamics, computer hardware and software, automatic control and machine vision. This paper describes the authors' experience in teaching a graduate robotics course at the University of Virginia and a short summer course at the Universidad de Deusto in Spain. Hands-on experience is a must in courses on robotics, and some simple yet effective systems designed and constructed by students are described. These include a program for transformation matrix manipulation, an operating system for manipulator control, and a simple three degrees of freedom programmable manipulator. The majority of the students who took both courses were electrical engineers, but mechanical engineers and computer scientists were also enrolled. Author Keywords: Robotics Education; Robotics Laboratory; Hardware; Software Development For Robotics Education *Parts of this paper were presented at the Second annual workshop on interactive computing, CAD/CAM: Electrical Engineering Education Washington,

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